A Microservice-Based System for Industrial Internet of Things in Fog-Cloud Assisted Network
Received: 4 February 2021 | Revised: 2 March 2021 | Accepted: 7 March 2021 | Online: 11 April 2021
Nowadays, the usage of the Industrial Internet of Things (IIoT) in practical applications has increased. The primary utilization is a fog cloud network, which offers different services, such as network and remote edges, at different places. Existing studies implemented the Service-Oriented Architecture (SOA) based on the fog-cloud network to run IIoT applications, such as e-healthcare, e-agriculture, renewable energy, etc. However, due to the applications' monolithic property, issues like failures, security, and cost factors occur, e.g. the failure of one service in SOA affects monolithic applications' performance in the system. With this motivation, this study suggests a microservice-based system to deal with the cost, security, and failure risks of IIoT applications in the fog-cloud system. The study improves the existing SOA systems for e-healthcare, e-agriculture, and renewable energy and minimizes the applications' overall cost. The performance evaluation shows that the devised systems outperform the existing SOA system in terms of failure, cost, and the deadline for all applications.
Keywords:IIoT, security, e-healthcare, SOA, microservice
S. Latif, Z. Idrees, J. Ahmad, L. Zheng, and Z. Zou, "A blockchain-based architecture for secure and trustworthy operations in the industrial Internet of Things," Journal of Industrial Information Integration, vol. 21, Mar. 2021, Art. no. 100190. https://doi.org/10.1016/j.jii.2020.100190
Q. Hao, S. Nazir, X. Gao, L. Ma, and M. Ilyas, "A Review on Multicriteria Decision Support System and Industrial Internet of Things for Source Code Transformation," Scientific Programming, vol. 2021, Jan. 2021, Art. no. e6661272. https://doi.org/10.1155/2021/6661272
S. Kunal, A. Saha, and R. Amin, "An overview of cloud-fog computing: Architectures, applications with security challenges," Security and Privacy, vol. 2, no. 4, 2019, Art. no. e72. https://doi.org/10.1002/spy2.72
A. Bamhdi, "Requirements capture and comparative analysis of open source versus proprietary service oriented architecture," Computer Standards & Interfaces, vol. 74, Feb. 2021, Art. no. 103468. https://doi.org/10.1016/j.csi.2020.103468
L. Li, H. Huang, X. Zou, F. Zhao, G. Li, and Z. Liu, "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, vol. 286, Mar. 2021, Art. no. 116483. https://doi.org/10.1016/j.apenergy.2021.116483
A. Lakhan and X. Li, "Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks," Computing, vol. 102, no. 1, pp. 105-139, Jan. 2020. https://doi.org/10.1007/s00607-019-00733-4
A. Lakhan and X. Li, "Mobility and Fault Aware Adaptive Task Offloading in Heterogeneous Mobile Cloud Environments," EAI Endorsed Transactions on Mobile Communications and Applications, vol. 5, no. 16, Jan. 2019, Art. no. e4. https://doi.org/10.4108/eai.3-9-2019.159947
D. K. Sajnani, A. R. Mahesar, A. Lakhan, and I. A. Jamali, "Latency Aware and Service Delay with Task Scheduling in Mobile Edge Computing," Communications and Network, vol. 10, no. 4, pp. 127-141, Oct. 2018. https://doi.org/10.4236/cn.2018.104011
Q.-u-A. Mastoi, A. Lakhan, F. A. Khan, and Q. H. Abbasi, "Dynamic Content and Failure Aware Task Offloading in Heterogeneous Mobile Cloud Networks," in 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), Al Madinah Al Munawwarah, Saudi Arabia, Feb. 2020, pp. 1-6. https://doi.org/10.1109/AECT47998.2020.9194161
S. F. Issawi, A. A. Halees, and M. Radi, "An Efficient Adaptive Load Balancing Algorithm for Cloud Computing Under Bursty Workloads," Engineering, Technology & Applied Science Research, vol. 5, no. 3, pp. 795-800, Jun. 2015. https://doi.org/10.48084/etasr.554
J. Uma, V. Ramasamy, and P. Vivekanandan, "Load Balancing Algorithms in Cloud Computing Environment - A Methodical Comparison," International Journal of Engineering Research, vol. 3, no. 2, pp. 79-82, Feb. 2014.
A. N. Saeed, "A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5986-5991, Aug. 2020. https://doi.org/10.48084/etasr.3666
Y. L. Ng, X. Jiang, Y. Zhang, S. B. Shin, and R. Ning, "Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4554-4560, Aug. 2019. https://doi.org/10.48084/etasr.2952
L. Bittencourt et al., "The Internet of Things, Fog and Cloud continuum: Integration and challenges," Internet of Things, vol. 3-4, pp. 134-155, Oct. 2018. https://doi.org/10.1016/j.iot.2018.09.005
M. Taneja, N. Jalodia, J. Byabazaire, A. Davy, and C. Olariu, "SmartHerd management: A microservices-based fog computing-assisted IoT platform towards data-driven smart dairy farming," Software: Practice and Experience, vol. 49, no. 7, pp. 1055-1078, 2019. https://doi.org/10.1002/spe.2704
C. Puliafito, E. Mingozzi, F. Longo, A. Puliafito, and O. Rana, "Fog Computing for the Internet of Things: A Survey," ACM Transactions on Internet Technology, vol. 19, no. 2, Apr. 2019, Art. no. 18. https://doi.org/10.1145/3301443
H. Chegini, R. K. Naha, A. Mahanti, and P. Thulasiraman, "Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy," IoT, vol. 2, no. 1, pp. 92-118, Mar. 2021. https://doi.org/10.3390/iot2010006
A. Kallel, M. Rekik, and M. Khemakhem, "IoT-fog-cloud based architecture for smart systems: Prototypes of autism and COVID-19 monitoring systems," Software: Practice and Experience, vol. 51, no. 1, pp. 91-116, 2021. https://doi.org/10.1002/spe.2924
S. Guo, K. Wang, G. Pau, and A. Rayes, "Edge Intelligence for the Industrial Internet of Things," IEEE Network, vol. 33, no. 5, pp. 8-10, Sep. 2019. https://doi.org/10.1109/MNET.2019.8863719
K. Janjua, M. A. Shah, A. Almogren, H. A. Khattak, C. Maple, and I. U. Din, "Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies," Electronics, vol. 9, no. 7, Jul. 2020, Art. no. 1172. https://doi.org/10.3390/electronics9071172
R. K. Naha et al., "Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions," IEEE Access, vol. 6, pp. 47980-48009, 2018. https://doi.org/10.1109/ACCESS.2018.2866491
B. K. Mohanta, D. Jena, U. Satapathy, and S. Patnaik, "Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology," Internet of Things, vol. 11, p. 100227, Sep. 2020. https://doi.org/10.1016/j.iot.2020.100227
L. Lu, L. Xu, B. Xu, G. Li, and H. Cai, "Fog Computing Approach for Music Cognition System Based on Machine Learning Algorithm," IEEE Transactions on Computational Social Systems, vol. 5, no. 4, pp. 1142-1151, Dec. 2018. https://doi.org/10.1109/TCSS.2018.2871694
F. Yang, Y. Zhang, B. Lv, and W. Dai, "A Task-Oriented Automatic Microservice Deployment Method For Industrial Edge Applications," in IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, Oct. 2020, pp. 2149-2154. https://doi.org/10.1109/IECON43393.2020.9254447
How to Cite
MetricsAbstract Views: 531
PDF Downloads: 397
Copyright (c) 2021 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.